{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# 五、支持向量机"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SetUP\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common imports\n",
    "import numpy as np\n",
    "import os\n",
    "from IPython.terminal.embed import InteractiveShellEmbed\n",
    "\n",
    "# to make this notebook's output stable across runs\n",
    "np.random.seed(42)\n",
    "\n",
    "# To plot pretty figures\n",
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['axes.labelsize'] = 14\n",
    "plt.rcParams['xtick.labelsize'] = 12\n",
    "plt.rcParams['ytick.labelsize'] = 12\n",
    "\n",
    "ip_shell = InteractiveShellEmbed()\n",
    "# Where to save the figures\n",
    "PRESENT_WORKING_DIRECTORY = ip_shell.magic(\"%pwd\")\n",
    "PROJECT_ROOT_DIR = \"images\"\n",
    "CHAPTER_ID = \"svm\"\n",
    "\n",
    "def save_fig(fig_id, tight_layout=True):\n",
    "#     path = os.path.join(PROJECT_ROOT_DIR, CHAPTER_ID, fig_id + \".png\")\n",
    "    path = os.path.join(PRESENT_WORKING_DIRECTORY,PROJECT_ROOT_DIR,CHAPTER_ID, fig_id + \".png\")\n",
    "    print(\"Saving figure\", fig_id)\n",
    "    if tight_layout:\n",
    "        plt.tight_layout()\n",
    "    plt.savefig(path, format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 线性支持向量机分类"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=inf, kernel='linear')"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:, (2, 3)]  # petal length, petal width\n",
    "y = iris[\"target\"]\n",
    "\n",
    "setosa_or_versicolor = (y == 0) | (y == 1)\n",
    "X = X[setosa_or_versicolor]\n",
    "y = y[setosa_or_versicolor]\n",
    "\n",
    "# SVM Classifier model\n",
    "svm_clf = SVC(kernel=\"linear\", C=float(\"inf\"))\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bad models\n",
    "x0 = np.linspace(0, 5.5, 200)\n",
    "pred_1 = 5*x0 - 20\n",
    "pred_2 = x0 - 1.8\n",
    "pred_3 = 0.1 * x0 + 0.5\n",
    "\n",
    "def plot_svc_decision_boundary(svm_clf, xmin, xmax):\n",
    "    w = svm_clf.coef_[0]\n",
    "    b = svm_clf.intercept_[0]\n",
    "\n",
    "    # At the decision boundary, w0*x0 + w1*x1 + b = 0\n",
    "    # => x1 = -w0/w1 * x0 - b/w1\n",
    "    x0 = np.linspace(xmin, xmax, 200)\n",
    "    decision_boundary = -w[0]/w[1] * x0 - b/w[1]\n",
    "\n",
    "    margin = 1/w[1]\n",
    "    gutter_up = decision_boundary + margin\n",
    "    gutter_down = decision_boundary - margin\n",
    "\n",
    "    svs = svm_clf.support_vectors_\n",
    "    plt.scatter(svs[:, 0], svs[:, 1], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(x0, decision_boundary, \"k-\", linewidth=2)\n",
    "    plt.plot(x0, gutter_up, \"k--\", linewidth=2)\n",
    "    plt.plot(x0, gutter_down, \"k--\", linewidth=2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(x0, pred_1, \"g--\", linewidth=2)\n",
    "plt.plot(x0, pred_2, \"m-\", linewidth=2)\n",
    "plt.plot(x0, pred_3, \"r-\", linewidth=2)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\", label=\"Iris-Versicolor\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\", label=\"Iris-Setosa\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_svc_decision_boundary(svm_clf, 0, 5.5)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"bs\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"yo\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "# save_fig(\"large_margin_classification_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 特征归一化敏感"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM 对特征缩放比较敏感，可以看到图 5-2：左边的图中，垂直的比例要更大于水平的比例，所以最宽的“街道”接近水平。但对特征缩放后（例如使用 Scikit-Learn 的 StandardScaler），判定边界看起来要好得多，如右图。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-2.0, 2.0, -2.0, 2.0)"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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3EEII4WFCQkL44x//yIIFCzh69Cj//e9/iYiIICYmxmrzz3/+k379+vH++++TmppqY7RCiDySeAshhBAeLDg4mD/84Q/MnTuX9u3bW9tnzJjBsmXLePzxx2ncuDG9e/fm//7v/0hJSbEt1unTITzcsTpweLjjtRBViSTeQgghRCU0d+5cpk+fzujRo6lWrRpJSUk8/fTThIWF8fzzz7s9nunTYcIEOHAAtHb8OWGCJN+iapHEWwghhKiEatWqxd133018fDzHjx/nu+++wzAMAgIC6NjRWq+On3/+mXfeeYfdu3eXazwvvwyFF+XMyHBsF6KqUJXh4YuuXbvqtWvX2h2GEEKUmFJqnda6q91xuJP02fbKyMjAy8uLatWqAfDggw/y+eefA9CpUycMw8AwDG688cYyfV8vL8dId2FKORYqE8ITlLbPlhFvIYQQogoJCAiwkm6AmJgY7rnnHmrVqsXGjRuZNGkSrVq1on379nz88cdl9r5hYSXbLkRlJIm3EEIIUYUNHTqUadOmcezYMebMmcO4ceOoXbs2W7Zs4eDBg1a748ePs2nTpmsuU/jmmxAQUHBbQIBjuxBVhSTeQgghhMDf399aDfPo0aPMnz+fBx980No/ffp0OnbsyI033shLL73E+vXrS5SEjx0Ln34KzZo5ppc0a+Z4PXZseXwaISomSbyFEEIIUYCfnx9Dhw6lefPm1rbc3FxCQkLYtWsXb731Fl26dOH666/nueeeo7hz9seOhf37HXO69++XpFtUPZJ4CyGEEOKq/vznP5OWlsaSJUt49NFHadCgAfv27ePdd99l8uTJVrvc3Fxy5WlJIVySxFsIIYRbpaWlsXPnTrvDENfA29vbWg3z0KFD/PTTTzzxxBOMHz/earN48WLCwsJ48sknWb58OTk5OTZGLETFIuUEhRDCRlWxnKBSSgO0b9/eKl3Xpk0bu8MSZeS5557j3XfftV43aNCAMWPGYBgGffr0wcfHx8bohCid0vbZkngLIYSNPDnxVko9BowD2gP/01qPK8731atXT2dnZ3P27FlrW+vWra0kvH379iilyiNk4QZaa9auXYtpmsTGxrJv3z5r380338zKlSttjE6I0pE63kIIIeySCrwB/Lck3xQeHs6xY8dITExk/Pjx1K1bl23btjFlyhQ6duxIq1atePnll9mwYcM1l64T9lFK0a1bN9555x327NnDunXrePHFF7nhhhu49dZbrXaHDx/mgQceYN68eVy6dMnGiIVwHxnxFkIIG3nyiHcepdQbQJPijngX7rOzsrJYunQppmkSHx/PiRMnrH3Nmze3RsK7du0qI+EeTGvNpUuX8Pf3B+D999/n8ccfByAoKIioqChiYmIYPHiw1UaIikammiCJtxDCc1WVxFspNQGYABAWFtblwIEDLttlZ2fz008/WUn40aNHrX1hYWFWEn7zzTfj5SU3bT3Z7t27+eabb4iNjWXLli3W9lq1ahEdHc3nn38uF1qiwpHEG0m8hRCeq6ok3vkVt8/OyckhKSkJ0zSJi4sjNTXV2te4cWOio6OJiYmhZ8+ekoR7uO3btxMXF4dpmmzcuJGIiAjmzp0LOH4OZs2axdChQwkMDLQ5UlHVSeKNJN5CCM8liXfx5ObmsnLlSkzTxDRNUlJSrH0NGzYsUDXD29u7RMcWFcvu3bvJyMigQ4cOACxdupT+/ftTvXp1IiIiiImJYcSIEdSoUcPmSEVVJA9XCiGEqPS8vLzo2bMn//jHPzhw4ACrVq1i4sSJhIeHk5aWxgcffED//v1p1KgRDz/8MIsWLSI7O9vusMU1uOGGG6ykGxwj3j169ODChQvExcVx5513EhISwujRo5k+fbr8PQuPIiPeQghhI08e8VZK+QA+wKtAE+CPQLbW+oqZUFn22Vpr1q9fb5Wu27Nnj7UvODiYUaNGYRgGAwcOxNfXt0zeU9gjJSXFmo6SlJQEOOb979+/35oLfu7cORkJF+VKppogibcQwnN5eOL9Go6kO7/XtdavXen7yqvP1lqzadMmKwnfsWOHta9OnTpERUVhGAaDBg2Sqhke7vDhw8yYMQMfHx8efvhhAI4ePUqzZs3o378/hmEwatQogoODbY5UVDaSeCOJtxDCc3ly4n2t3NFna63ZunUrsbGxmKZJcnKyta9WrVqMHDkSwzAYOnQo1apVK9dYhHvMmjWLMWPGkJubCziWtx8wYICVhNevX9/mCEVlIIk3kngLITyXJN7usW3bNmuawq+//mptr1GjBpGRkRiGwfDhwwkICHBrXKJsHT9+nFmzZhEbG8uPP/5ITk4OAP7+/hw/fpyaNWvaHKHwdJJ4I4m3EFVCdjakpEBqKly6BH5+0KgRNG0KPj52R3fNJPF2v127dllJ+Lp166ztAQEBjBgxAsMwiIiIkLnCHu7kyZMkJCRgmiZKKebMmQM4KuRER0fTv39/xowZQ5MmTWyOVHgSSbyBLl266DVr1kgdVyEqI61hyxbYtcvx2jmCBUBe2bgWLaBdO/DAxTYk8bbXvn37rBKFq1evtrZXq1aN4cOHYxgGkZGR1KpVy8YoRWnl5uZaOcKKFSvo06ePta9nz54YhkF0dDRhYWF2hSg8hCTegFJKO//Ex8fH+qpTpw75V0fr378/hw8fLtDG29sbHx8f7r33Xh555BEANm7cyKRJkwq0y//117/+1ZorNm3aNLZs2eKy3XXXXYdhGABcunSJ6dOnF3jP/F+dO3emYcOGgOOhkUOHDrk8pq+vb4GOIT09HS8vL+uY3t7estKXqDy0hqQkOHasYMJdmLc31K8PvXp5XPItiXfFceDAAeLj4zFNk59//tna7ufnx9ChQzEMg5EjR1K7dm37ghSllp6ezpw5czBNk8TERC5evGjt6969OwkJCYSGhtoYoajIPDLxVkrdieNJ+DDgCDBOa71cKTUQ+MC5fZVzu+t1hQsez+WHqFu3LidPnrReh4eHU9QyxS+++CJ//etfAVi4cCFDhw4t8v327dtHeHg4ANHR0cTHx7tsN3jwYBYuXAjAqVOnrvh0dWxsrJWkv/3227z44osu29WpU4dTp05Zr6+77jr2799foI23tzfe3t4899xzTJkyBYBly5Zx9913F3kxkZCQQOPGjQGYPHkyK1ascNmuQ4cOvPDCCwCcP3+eF1988bILibzX0dHRtG7dGoD169ezevVql8esXr06I0aMsOJfuXIlWVlZLo8ZEhJCSEgIAJmZmZw9e9blMeXuRyWxebNjpPtKSXceb2/HyHf79uUfVxmSxLtiOnz4sJWEL1++nLzflb6+vgwePBjDMIiKiqJu3bo2RypK49y5cyQmJmKaJnPnzqV27dqkpKRYv0OmT59O9+7dadGihc2RioqitH222ydGKqUGA+8AdwCrgYbO7fWAeOBBYDYwBfgO6HG1Y3bp0oW1a9eSm5tLdna29ZVT6Jf1smXLyMzMLNAm76tRo0ZWu06dOpGQkGAdo3DbevXqWW3vvfdeunfv7vKYN9xwg9XO19eXcePGXdYm7/h5o90ADRo0KPKYhW93BgQEEBgYWOB4eV/5L6oyMjIKLLdcWP5ztXHjRn744QeX7U6dOmUl3hkZGfz73/8u8pht2rSxEu958+YxadIkl+2Cg4M5ceKE9fqOO+7g4MGDLtvmv0BaunQpw4YNc9lOKcXevXutC6T777+fxMREl0l63759+eSTTwA4c+YMkZGRlyX8eV/PPvssPXo4fiQTExOZNWuWy2PWrFnTOk8An332GRkZGZe18/b2pn379tZiEcePH2fdunVFXiC1adMGPz8/q21mZmaRbX08eN4z4JjTXSjpnr68KS//rz0HTwYQFpzBm3dtZmwf5wqGOTmO9q1be/Scb1ExNG7cmMcff5zHH3+cI0eOMGPGDGJjY1m2bBmJiYlWf5K/akbeoIDwHDVq1OD222/n9ttvJyMjg127dllJ9/Hjx7n//vvJycmhQ4cOxMTEYBgGrVq1sjlq4cncPuKtlPoZ+Fxr/Xmh7RNwjHD3dL4OBE4AnbXW2690TE8YPXEXrbWVzHt5eVlJ2sWLFzlx4kSRFxPt27e32v76668cPXrUZeIfGhrKwIEDAUfi/dlnnxV5zLFjx9KmTRvAkaTmXcwU/goMDOTLL7+0PkNMTAxHjhxx2fahhx7iqaeeAmDRokXcddddLt9ba01KSor10ExUVBQJCQkuz1lERARz584FHHVgGzRoUOT5nTFjBqNGjQJgypQp/OUvf3HZLiQkhGPHjlmvmzZtyqFDh1y2nTRpknVnIjExscDof2EHDx6kadOmAIwePZqZM2e6bDds2DDmzZsHOH55hIeHF3kx8fHHHzNkyBDAcYHwySefuEzig4OD+frrr633eOqpp1zecfD29mbw4MHWXaM9e/YQGxvrcoqVj48PMTEx1kNsq1at4tixY47jHD+Oz759+AA+3t4kbb+B12INMi75ANnAXqr5wttjtxLT4yjeXl74+Pnh06kTgW3besyFh4x4e5Zjx44xc+ZMTNNk8eLF1qCFl5cX/fr1wzAMRo8efcV+RHiG/fv388orr5CQkMBvv/1mbW/bti2GYfDYY48VGIgTVYNHjXgrpbyBrkCCUmo3UA2YCTwLtAWsGk9a6/NKqT3O7VdMvMXv8s9zz69atWrFfnK7Y8eOxWoXEBDAE088Uay2ERERREREFKttbGxssdoNGjSI48ePu9yXm5tbYK77119/zcWLF10m89WrV7fa1alTh+XLlxd5Z6Jbt24FPlP9+vVdHrNwXeDx48dz+vRpl23zL40cHBzMkCFDLnvfvK/8i37UrVuXxo0buzxm3kUUQHZ2NhkZGUWex8zMTOv/Dx06RFEJUeFE4vvvvyctLc1l28DAQCvx3r59e5FTpwCGDBliJd5vvvkms2fPdtmuuu9QLmTd6Xx1AriRi1nw1BeOr/xmzpxJVFQU4Jg6NXnyZJdJf4MGDdi4caP1fQMHDuTkyZMuLxLuu+8+7r//fsAxdeqdd94p8mJi8uTJ1jzgb775hl27dhX5HIjwLPXr12fChAlMmDCBkydPMmvWLEzTZNGiRSxevJjFixfz6KOP0rdvXwzDYMyYMQXuqArPER4ezrRp08jMzGTRokWYpsnMmTNJTk5m27ZtPProo1bbI0eOEBoaKs9Yiaty95BQKOALGEAfIAuYBUwCagCFs6izgMuim84R8gmAPIUsLlN4jnfNmjWLVb/Vz8+P3r17F+s9unTpQpcuXYrV9vXXXy9Wu5tvvpkFCxYUq+3nn39+9UY4EuZz5865TNBzcnIKPEQ0YcIERowY4bJt4Yu5f/3rX0Ue95ZbbrHaNW/enOeff77Ii4nAwECrbffu3QHHxUL2sWNkX7xIdm4u2Tk5/LKzbaFP1gLHyHc2DeucIzsnh+ycHHKgwAVK/ilY+S8ywPE8RH7JyckcPXrU5Xns27ev9f+HDh3i+++/L+qU89JLL1n//80331h3VAqLjIws8hii4gsODmb8+PHWhfXs2bMxTZMFCxawbNkyli1bxuOPP06vXr2sqhl5d6yE5/D392fEiBGMGDGCTz75hCVLlrB582ZralFubi7dunXD398fwzAwDIMuXbpIEi5ccutUE6VUHeAUjiklXzq3ReNIvH8CfLXWj+Rrvxl4TWsdd6XjevJtSyFEEZKSHDW7ncIfieDAicDLmjWrd579Hyb+vqFRI0d1E6fc3Fwr8XY1JSn/8xXbtm0r8BxI/u8JDw/n+uuvBxwP3q1YsaLIi4kHH3zQWojlm2++YceOHS4vUNq1a8cjjzwiU00qmd9++81KwufNm1fggu/mm2+2knC541E57N+/n+7duxe4AxseHm4l4d27d5ckvBLxuKomSqkU4GWt9VfO13mJ90fA/VrrXs7tgThGwG+SOd5CVEH79sGGDdbDldOXN2XCJ12dc7wdAvyy+fShtb8/YOntDZ07gwclNDLHu3JLT08vUDXjwoUL1r4uXbpYyVn+h/GF58nJyWH58uWYpklcXBxHjhyx9q1YsYJe+QYDhGfzxMR7MjAcGIFjqkkCsBR4D9gNjAfmAq8Dt2qtr1rVpCp14kJUGdnZkJBQ/Kom4Ei8R470qKomknhXHefPn2f+/PmYpsns2bM5f/68ta9Tp05WEn7jjTfaGKUordzcXH7++WdM0yQpKYmVK1da09rGjRtHzZo1MQyD3r17XzbdTVR8nph4+wL/Au4GLgLfA89prS8qpQYB7wPN+L2O9/6rHbOqduJCVHpSx7tSkj4bLly4wMKFC4mNjSUhIYH09HRrX7t27awkvE2bNjJNoZI4deoUoaGhZGdnAxAaGsqYMWMwDIO+fft6TCWmqs7jEu/yIJ24EJWUrFxZKUmfXVBmZiY//PADpmkya9Yszpw5Y+1r1aqVlYR36NBBknAPprVm7dq1mKaJaZrs3bvX2levXj1iY2Pp16+ffQGKYpHEGwgICNBt2rS54kIiRS3VXpHbSQcrBI7ke8sWx8g3FEzAfXwc+1u0gHbtPC7pBkm8RUGXLl1i8eLFmKbJjBkzCqxU3KJFCysJ79y5s/yO8GBaazZu3IhpmsTGxrJ7925SU1Otsq3ff/89NWrUYNCgQQXKwwr7SeJN0UvGezovL69SJfIV6SLiWtt5eXnJLxfhkJ0NKSmOSidZWeDr66hg0rSpR83pLkwSb1GUrKwsli1bhmmaxMfHF6iacd1111lJeLdu3aSf9GBaa/bu3WtVTdJaEx4ezsGDBwkKCmLkyJHExMQwePDgy9aIuKL8fealS+DnVyn6TLtJ4g20adNGf/XVVy7LdRW1EIontBMOdl4YVJSLksJ1yUXlIYm3KI7s7GyrakZ8fHyBqhlhYWFER0djGAY9evSQ/sLDXbx4kXfffRfTNNm0aZO1vWbNmtx2221MnDiRzp07F32AK90lzHuY04PvEtpNEm8qZyeutSY3N7fUiXxFuYi41na5ubl2/1VUCPlXJK0oFwbufm9vb+9KOaonibcoqZycHKtqRlxcHIcPH7b2NW7c2ErCe/bsKVUzPNzOnTutOeEbNmwA4Mcff2TAgAEAHDhwgHr16v2+EFkVeC7GbpJ4I514ZZa3+ImdFwZ2X5RkZWXZ/ddQYeQl5hXhLkRZXbzcdNNNkniLa5abm8uqVauIjY3FNE1SUn4vr9mgQQOrakafPn3w8ZHpBZ5sz549zJo1iyeeeML6uxwxYgRLliwhIiICwzAYER5OzdTUSl0Jym6SeCOduKj8rnT3o6pclOQU5xeJZ5LEW5QJrTVr1qyxRkj37dtn7QsJCWH06NEYhkG/fv3w9fW1MVJRFnJzcxkwYADLli2ztvn7+jKsY0eMHj24rWtX5qy7sdKtfWA3tyXeSqmFwGAgWmsdn2+7AqYC9wPvaK1fuNZgrpVHd+LZ8vCDEMWhtS6QlFfEqVHX0m7jxo2SeIsyp7Vmw4YNBapm5AkODmbUqFEYhsGAAQOkaoaHO3ToEHFxcZhff03SunXk5XX39n2auJX/r9Kt9ms3dybeHYH1wA6gvdY6x7n978Cfgf9orSdcayCl4ZGduDz8IIRA5niL8qe1ZvPmzVYSvn37dmtf7dq1iYqKwjAMBg8ejL+/v42RilJJSiJ1yxZmrF6NuWoVu1JncPh0C+fOV4A1QAxN6g4i5ePVv39fo0aOud6iWNw61UQp9QWOke0/aK2/UEq9BLyJY/XJu7TWtjwJ53GduDz8IIRwksRbuFtycrI1HWXLli3W9lq1anHbbbdhGAZDhw6levXqNkYpSmzJEjhxwnrpdYeB1nm5w43ATuf/ezOofRuMHj0Y1a0boS1agCzcU2zuTrybALuAo8DfgH8DC4CRWutL1xpEaXlcJ14FlsEWQhSPJN7CTtu3b3dMUzBNNm7caG0PDAwkMjKSmJgYhg8fTkBAgH1BiuJJSnJMW3UKfySCAyec1U44AcwCYoEfAUfJYi+leOdPf2LiBx+4OVjP5faHK5VSbwF587h/BgZrrTMKtekLTAS6AI1wjpBfa5BX41GdeHY2JCRYSXfSH4PJOnt5zVXfoFx6/eek44U8/CBEpSWJt6godu/ebSXh+f9+AgICfq+aMWIENWrUsDFKUaR9+2DDBiu/mL68KRM+6XrZHO9/3P8j/r4JmCtXsnDTJuZ+/jmD77sPgIULF7Jt2zaio6Np0qSJLR+jorMj8f4z8Hfny9Za6+0u2kQAvXHMCf8KeKQ8E++QkBAdExNzWZmuwMBAXn75Zavdf//7X9LT012W/Wrbtq1VkP7EiROsXbu2yPJfrVu3tubBHTt2jMzMzCLbXvbkeKF/GEtvDynyc/X73rlKmTz8IESlJYm3qIj27dtnJeGrVq2ytlerVo1hw4ZhGAaRkZEEBQXZGKUooNDAHjiS7ytVNTlz8SI1YmLwca6IGRUVRUJCAgC33HILhmEQHR1Ns2bN3PtZKjB3TzW5C5iOY6pJA+BjrfWfrvI954DHyjPxLmrJ+Nq1a3P69GnrdfPmzQuUV8rv+eef5+233wbghx9+YMiQIUW+3549e2jevDkAMTExmKbpst3AgQNZtGgRAGfOnKFRo0b4KIW3Uvh4eeHj7U3OGV+88eZJnqQHPQCYz3xmMpM6LXLx8fZ2fFWvjk+9egQFBfHtt99a7/HnP/+ZU6dOubyYGDhwIBEREYCjE/3222+LrDVsGAa1atUCYPXq1Rw5csTlhUSdOnVo3bo14FjEYffu3UXWLg4MDJSSVUJchSTeoqI7ePAg8fHxmKZJUlKStd3Pz48hQ4ZgGAYjR46kTp06NkYpgFJPZY2Li+N///sfiYmJXLhwwdrerVs3nn76ae66667yiNqjlLbPLvbcBeco9pdAMjAA+Al4UCn1L1ej3u4UFhbG888/f1mZrsJJ3x/+8AdOnDjhsqRXp06drHZ16tRhyJAhRZYEq+a8MgSoW7cuTZo0cXnM/E+HZ2dnF/ghLuwSv0+RP8EJdrDDMZu+kODg4AKv4+LiOHjwoMtjVqtWzUq8d+7cyUsvvVTk+w8YMMBKvN955x3i4+Ndths8eDALFy4EHBcTrVq1KvKYpmkSHR0NwFtvvcUrr7ziMkmvV68eycnJ1vcNGTKEo0ePurxIGDt2LA888AAAv/76K2+88UaRdxtee+0163x9++237Nixw2W7Zs2aERkZCUBmZiYzZswo8mKiXbt2hIQ47lIcPXr0sjjz3+nIaweQlZWFt7e3LOUshPA4YWFhPPXUUzz11FMcPnyYGTNmYJomP/30E3PmzGHOnDn4+voyaNAgDMMgKirqst9Vwk3atYOzZ4tfvKFduwKbo6OjiY6O5ty5c8ybNw/TNJkzZw5r1qzhyJEjVrvDhw9z/vx5WrZsWV6fpNIqVuKtlOoNmMAhYIjW+rhS6hUcs/TfBkaVW4TFEBISwiOPPHLVdq+88kqxjte1a1cWLFhQrLaffPJJsdoFBwdz/vx5slesIPvwYbJzcsjOyWHFw3XIIYfa1LbaDmc4XelKxzdOWu2y69Qh+8YbL0vc/vGPf5Cenu4y8b/55putduHh4bzwwgtFXkzUrFmzwOfPyspyecw2bdoUeP+WLVsWWb+48IVHTk4OOTk5ZGZmFjhG4bsu27Zt49ChQy7PY8+ePa3/T01NLfJuA8Bzzz1XIPGeNWuWy3bDhg2zEu+zZ89e8Yo+Li6OMWPGAPDZZ58xadIkl+3q1avH8ePHrdfXX389KSkply397u3tzcSJE60pUcuWLeOBBx4o8mLCNE0aNGgAwF//+ldWrVrlsl2HDh148sknATh37hxTpkwp8mJi5MiR3HDDDYDjYmbjxo0uj1m9enVrmWKADRs2kJOT43LVxjp16lC7dm3AcdGRmZlZ4H0r49LvovjOnj3LxYsXCwxiCM/QuHFjHnvsMR577DGOHDnCzJkziY2NZenSpcybN4958+YxYcIEBgwYgGEYjBo1ivr169sddtWhlKMSWlHlin18HJXVrlKuuEaNGsTExBATE0NGRgYLFiwokFN8+OGH/PWvf6VDhw4YhoFhGNbdcHFlV51q4qzfvQy4APTWWu/Jt28N0BXoq7VeXsT3l/tUE4+6bVlF53gXXvwkf5Kem5tLw4YNrbY7duzg4sWLLhP/Zs2aWUliWloay5cvL3LBkj/84Q/WQ0Dffvst27Ztc3mB0Lp1ax599FHAMYr/8MMPF3kxMWXKFHr37g04Lro++OADl3HWrVu3QJmuZs2aFXln4i9/+Quvv/46AHPmzOG2224r8jweOnSIxo0bAwXn4hUWERHB3LlzAThy5EiB81vYzJkziYqKAmDy5Mm8+uqrLtuFhoYWGPFo0qQJhw8fdtl20qRJTJkyBYDExERGjBhRYL+Xl5eVhO/YscN6iGf8+PEsWrTIZeLft29f/u///g+A06dPc8cdd7hcmt3b25unn36aLl26ADB//nzmz5/v8pi1atXiiSeesOKaNm0aFy9edHnM1q1bW79YTp48SXJysst2Pj4+NG/e3LrjdubMGZcXKF5eXiilquRUE6WUrlmzplW6btiwYVK6zsMdP36cmTNnYpomP/74o7XSrJeXF7feeiuGYTBmzBhr4EC4Qf4F+rKywNe3zBbomzRpEu+//z5nz561trVp0wbDMLj99ttp27ZtaaOvsMp1jrdS6gZgBeAP3Kq13lRo/yDgB2CV1rpHEceQxDs/qWpSpbla+t3f35/AQEfJp/Pnz5OamlrkxUS3bt2sOwmrV68mLS3NZeLfuHFjhg0bBkB6ejoffvhhkasrjh8/nnbO240zZ85kxowZLtsGBQXxzTffWJ9lxIgRHD161GXbJ554gj//+c+AI/G+/fbbC7xnfqmpqdaFQWRkpHXBUNhtt91mXWikpqZaFyCuJCQkWBcwr776KpMnT3bZrkGDBqSlpVmvGzVqVOB1fq+++iqvvfYacPULpMOHD9OoUSMr7jlz5hT5mWbPnl3lEu+AgACdf+pdYGAgI0aMwDAMRo8ejY/0dR7t5MmTJCQkYJomP/zwA1lZWQAopejTp4+VhF/p37Co+C5dusSiRYswTZOZM2daz9SNHz+ezz//HHA8C5Y3yFBZuL2qSYnfQBLvy0kdb1GFaa0LXIBUr17dmkJ14sQJMjIyXCbzNWvWpEULxypsFy9eLHC3o/DFRL9+/axR9F9++YWVK1e6vOMSEBBQ4NmH5557jtOnT7s87p133skdd9wBwIoVK3jxxReLvIuTlJREvXr1ALj77rtZsGDBZe1yc3MZOXIkCQkJVS7x7tq1q/7uu++sqhlr1qwBoGHDhhw6dMj6eZDpKJ7vzJkzzJ49G9M0mT9/Ppcu/f48U8+ePa2qGWFhYTZGKUorKyuLJUuWYJomd955pzUt8X//+x+vvPKKNR2lS5cuHp+EV8jEWylVA7jB+fJnHPPAE4BTWmvX99tLweMSb1m5UogqLzc3l9zcXHx9fatk4p2/zz5w4ABxcXF4e3tbzyacOHGC8PBwBg4ciGEY3HbbbdZzA8Iz/fbbb8yZMwfTNJk3bx4XL1609nXv3t1Kzq7z4GmVoqBx48bx5ZdfWq+bNWtm/T13797dIwsOVNTEux+wxMWuL7XW48r6/Twu8QZH8l3Khx+EEJ7P0+d4K6XqAp8DQ3Asj/ei1vqbK31PcfrshIQERo0aZT187evrW6B0Xd26dcvmAwhbnDt3jsTEREzTZO7cuWRk/L4OX5cuXayR8Ly7XMIz5d0BNE2TuLg4UvOtrJl/+qAnqZCJt7t5ZOKdpxwffhBCVHyVIPH+H+AFPAB0AuYCPbXWyUV9T3H77LS0NKt+9E8//URubi4A/v7+HD16VBZvqSQyMjKYP38+sbGxzJkzh3Pnzln7OnbsaI2QXql8raj4cnNz+eWXXzBNE9M0eeKJJ3j22WcB2Lp1Kx999BGGYdC7d2+8vb1tjrZoknjj4Ym3EKJK8+TEWykVCJwG2mmtdzq3TQMOa61fKOr7rqXPPnr0qFU1AxwLnYHjl/nYsWO59dZbGT16NKGhocU/aP6Bj0uXwM9PBj5sduHCBRYuXIhpmiQkJPDbb79Z+9q2bWsl4W3btvX4ucJVWW5uLllZWVaxgPwPwdevX58xY8ZgGAa33nprhXvYWhJvJPEWQnguD0+8OwM/a62r59s2EUcVrCLLvpS2z87JybFGxFatWkWPHo6iWl5eXvTt29eqmlFkGc0rTfXLG2mTqX62y8zMLFA148yZM9a+Vq1aWUl4hw4dJAn3cJs3b+abb74hNjaWPXusqtUEBwczbtw4/va3v9kYXUGl7bM9b1a7EEKIiqIGcLbQtrNAzcINlVITlFJrlVJr8y8udS3y34Zu1aoVU6dOZcSIEXh7e7N06VIee+wxGjduTJ8+fS4vD5n3cHteZanCD7jnbdu1y9GuEgxOeSp/f39GjBjB1KlTOXr0KPPnz+fBBx8kODiY7du388Ybb9CpUydatmzJiy++yLp16y5bkE14hvbt2/PWW2+xa9cuNmzYwMsvv0zLli05efJkgcXozp8/T2JiYoHqOJ5GRryFEMJGlWDEO0lrHZBv2zNAv/Ic8S7K2bNnC5SuCwoKIjU11UrUTdOka40ahGdkSDlXD5adnc3SpUsxTZP4+PgCiVl4eHiBqhkyEu65tNYkJydbC5gBxMbGcvvttxMUFMTIkSMxDIMhQ4a4teyoTDVBEm8hhOfy8MQ7b453W631Lue2r4DUsp7jXVLp6ens2LGDrl0dp/b06dPUr1+f7Oxsul5/PTE9ehA+axj1zzW57HtlATPPkZOTw/Lly62qGflX123atCnR0dHExMTQo0cPjyxdJwqKj4/n9ddfZ9Om39dzrFGjhrUK7ujRo8v9YksSbyTxFkJ4Lk9OvAGUUt8CGngQR1WTRMqoqklZ2r9/P88/9hhzf/yR8/nqR7egBX3py23cRhC/V0np971zFNXbGzp3BqktXeHl5OQUqJpx+PBha1+jRo2Ijo7GMAx69epVoatmiKvbuXOntQDX+vXrAbjppptYt26d1SYjI4OAgICiDnHNJPFGEm8hhOeqBIl3XeC/wGDgJPBCWdTxLhdJSWTs28eCX3/FXLmSmSvWk0EGCsX3fE89HKuNnuUsUd/nm0PaqJFjITPhMXJzc1m9ejWxsbGYpsnBg7+v3RcaGmpVzejbt2+Fq5ohSmbv3r3ExcXRoEED7r33XsBRnrBr164MHz4cwzCIjIykZs3LHj25JpJ4I4m3EMJzeXrifS1s67OXLIETJ6yXC28PYi1r2cMe7sXxC1ujuZu7qdPYG6NHD2JuuYV2N92E6t/f/fGKMqG1Zu3atdZI+N69e6199erVY/To0RiGQf/+/fH19bUxUlFWPvvsM/74xz9ar/39/Rk6dGiZrILrsYm3UqoFsBkwtdb3OLcNBD4AwoBVwDit9YGrHUsSbyGEp5LE242Skhw1u52W3h5yWZOjHOWP/JF00q1tLZs2xbj3XgzDoFOnTvLAngfTWrNx40ZM0yQ2NpZdeSUlgbp16zJq1CgMw2DgwIH4+fnZGKkorUOHDlkLcK1YscKqeBMcHMyRI0eu+U6HJyfeC4HqwAGt9T1KqXrAHhzzBGcDU4A+WuseVzuWJN5CCE8libcb7dsHGzZYFU1cJd4A2WST+/JSzJUrmbFmDSfyLeKydOlSbr31VreEK8qX1potW7ZYSfi2bdusfUFBQURFRWEYBoMHD3Zr1QxR9tLS0pgxYwamadKkSRO++uorwDEP/M4772TkyJGMGjWKevXqXfVYHpl4K6XuBMYAW4EbnIn3BBwj3D2dbQKBE0BnrfX2Kx1PEm8hhKeSxNuNsrMhIcFKvJP+GEzW2csrXeSvapIN/FS7NuaMGfz0009s3LjRGil7+OGHCQwMxDAMbr75Zqma4eG2bt1qTUfZvHmztb1mzZpW1Yxhw4ZRvXr1KxxFVHS5ubnWv9X4+Hiio6MBx/oA/fr1s6qjFLUKrscl3kqpWsBaYCDwAL8n3v8C/LTWf8rXdgvwqtY6zsVxJgATAMLCwrocOHDVGSlCCFHhSOLtZps3/754ztUUquOttbammZw9e5b69etbC3k0btzYqprRs2dPqZrh4Xbs2GFVzdiwYYO1PTAwkMjISAzDYPjw4QQGBtoYpSitU6dOWSPhixYtIjs7G3CsgtunTx/mzp172d+xJ65cOQX4XGudUmh7sVdAA9Baf6q17qq17hoS4vp2oRBCCFFAu3ZQv/7vS8MXxdvb0a5dO2tT/rndNWvWZPHixTz11FM0adKEw4cP895779G3b1+aNGnCkiVLyusTCDe48cYbeemll1i/fj27d+/m7bffpmvXrpw/f57vvvuOmJgYQkJCMAyDb7/9lvT09KsfVFQ4devW5YEHHmDevHkcO3aML774gsjISHx8fDh9+nSBpPvLL78kJaVw6lpybh3xVkp1AqbjmD5ySSn1GgVHvH211o/ka78ZeM3ViHd+MtVECOGpZMTbBlrDli2OkW8oOPrt4+PY36KFI+kuxoOUubm5rFmzxpqmsH//fg4ePEjTpk0BmDlzJjVq1KBfv35Sus7D7d+/3xoJX7lypbXd39+fYcOGWVUzgoKCrnAUUdGdPXuWlJQU2jkvvHfu3MmNN96Yt9tzppoopZ4C3gTrcfEagDewDfgYuF9r3cvZNhA4Dtwkc7yFEJWVJN42ys6GlBRHpZOsLPD1ddTsbtr0mleq1Fqzbds22rRpY71u2bIlu3fvJjg42CpdN2DAACld5+FSUlKsqhlJSUlW1Qw/Pz+GDBmCYRiMHDmSOnXq2BypKK3k5GReffVVFi9ezOnTpz0q8Q4AauXbNBEIB/Lmde8GxgNzgdeBW6WqiRCiMpPEu3LLzMxkypQpxMbGsnPnTmt7nTp1iIqK4sknn6RTp072BSjKRGpqqjVX+KeffiI3NxcAHx8fBg0ahGEYREVFFatqhqi4srKy8PPz85zE+7I3zzfVxPl6EPA+0Izf63jvv9pxqlInLoSoXCTxrhq01iQnJ1vTUZKTkwGYP38+Q4cOBeDw4cMEBwdL6ToPd/ToUSsJX7JkiZWEe3t7079/f6tqRv369W2OVFwLj6tqUh6qYicuhKgcJPGumrZt28bMmTOZOHGiNeVk9OjRLFq0iMjISGJiYhg2bBgBAQE2RypK4/jx48yaNQvTNPnxxx8LVM3o27cvMTExjB49moYNG9ocqSguSbyRTlwI4bkk8RbgeECzb9++JCUlWdsCAgIYMWIEhmEQERFBjRo1bIxQlNapU6dISEjANE0WLlxIVlYW4KiW07t3bwzDYMyYMTRp0sTmSMWVSOKNdOJCCM8libfIb+/evVbVjNWrV1vb//a3v/HMM8/YGJkoS2fOnGH27NmYpsmCBQvIzMy09t1yyy0YhkF0dDTNmjWzMUrhiiTeSCcuhPBckniLohw4cID4+HhiY2P55ptvCA8PB+CNN95g9erVVtWM2rVr2xqnKJ3ffvuNuXPnYpomiYmJXLx40drXrVs3DMPAMAyaN29uY5QijyTeSCcuhPBckniLkmrbti1bt24FwNfXl8GDB1tVM+rWrWtzdKI0zp07x7x58zBNkzlz5pCRkWHtu+mmm6wkvEWLFjZGWbVJ4o104kIIzyWJtyipI0eOMGPGDGJjY1m2bFmB0nVvvfUWEydOtDlCURYyMjJYsGABsbGxzJ49m3Pnzln7OnToYCXhrVu3tjHKqkcSb6QTF0J4Lkm8RWkcO3aMmTNnYpomixcvZtasWYwYMQKApUuXsn37dkaPHk1oaKjNkYrSuHjxIgsXLsQ0TWbNmsVvv/1m7WvTpo2VhLdr1w5VjNVWxbWTxBvpxIUQnksSb1FWTp48Sc2aNfHz8wPAMAzi4uJQStG3b1+rakajRo1sjlSURmZmJj/++COmaTJz5kxOnz5t7bvxxhutJLxjx46ShJcDSbyRTlwI4bkk8Rbl5bvvvuPrr79m4cKFXLp0ydreq1cvHn/8ce644w4boxNlISsriyVLlmCaJjNmzODEiRPWvuuvv95Kwrt06SJJeBkpbZ/tVZbBCCGEEKJiuOOOO5g9ezbHjh3j66+/ZtSoUfj7+5OUlMT+/futdkePHi3wWngOX19fhgwZwqeffkpaWhqLFi3i4Ycfpn79+uzZs4d33nmHbt26cd111zFx4kRWrlxpPRMg7CEj3kIIYSMZ8RbulJ6eTmJiIj179qRp06YAvP7667z22mt07drVqh99ww032BypKI2cnBxWrFiBaZrExcWRlpZm7WvSpAnR0dHExMRwyy234OUlY7AlIVNNkE5cCOG5JPEWdnvppZd47733OH/+vLWtU6dOGIZBTEwMLVu2tDE6UVq5ubn88ssvxMbGEhcXx6FDh6x9DRs2JDo6GsMw6N27N97e3jZG6hkk8UY6cSGE55LEW1QEFy5cYMGCBZimSUJCAunp6QDcc889TJs2DQCttcwT9nC5ubmsXr0a0zQxTZMDBw5Y++rXr8+YMWMwDINbb70VHx8fGyOtuCTxRjpxIYTnksRbVDSZmZn88MMPmKbJXXfdxdChQwGIi4vjlVdesR7Ya9++vSTiHkxrzbp16zBNk9jYWPbu3Wvtq1evHqNHj8YwDPr374+vr6+NkVYskngjnbgQwnNJ4i08xfjx45k6dar1ukWLFlYS3rlzZ0nCPZjWml9//dVKwnfu3Gntq1OnDqNGjcIwDAYNGmSVq6yqJPFGOnEhhOeSxFt4iqysLJYtW0ZsbCwzZszg+PHj1r6RI0cya9YsG6MTZUVrzZYtW6zpKFu3brX2BQUFMXLkSAzDYMiQIVSrVs3GSO0h5QSFEEIIUe58fX0ZNGgQn3zyCampqSxevJhHHnmE0NBQbr75Zqvdrl27eOaZZ/jll1+kdJ0HUkrRvn17Xn/9dZKTk0lOTmby5Ml06NCBs2fPMm3aNKKioggJCeHuu+9mxowZXLhwwe6wPYaMeAshhI1kxFt4upycHDIzMwkICADgzTffZNKkSQA0btzYqprRs2dPqZrh4Xbu3ElcXBymabJ+/Xpre2BgICNGjMAwDCIiIggMDLQxyvIlU02QTlwI4bkk8RaVzcaNG5k2bRqmaXLw4EFre4MGDbjvvvt45513bIxOlJW9e/da01HWrFljba9evTrDhw/HMAwiIyOpWbOmjVGWPZlqIoQQQogKo1OnTvz9739n//79rFq1imeffZbw8HCOHDlCSkqK1e7ChQssWrSI7OxsG6MV16p58+Y899xzrF69mv379/P3v/+dW265hQsXLhAfH8/dd99NSEgIUVFRTJs2jbNnz9odcoUgI95CCGEjGfEWVYHWmvXr1+Pn50f79u0BmDVrFqNGjSI4ONiqmjFgwIAqXzXD0x06dIj4+HhM02TFihXk5Zl5y9sbhsHIkSOpW7euzZFeG5lqgnTiQgjPJYm3qKri4uKYNGkS27dvt7bVrl2bqKgoDMNgxIgRUqLQw6WlpTFjxgxM02TZsmXWw7Y+Pj4MHDgQwzAYNWoU9erVsznS4pPEG+nEhRCeSxJvUdUlJydbc4W3bNkCQPv27dm0aZPVJjMzE39/f7tCFGXg6NGjzJw5E9M0WbJkCTk5OQB4e3vTr18/DMNg9OjRhIaG2hzplUnijXTiQgjPJYm3EL/bvn07cXFxNGjQgAceeACA3bt307lzZyIjIzEMg+HDh1sVVIRnOnHiBLNmzcI0zQLz/L28vOjbt6+VhDdq1MjmSC8niTfSiQshPJck3kJc2X//+18rCQcICAggIiLCmo5So0YNG6MTpXX69GkSEhIwTZOFCxdy6dIlwFFPvFevXhiGwZgxY2jatKnNkTpI4o104kIIz+WpibdS6jFgHNAe+J/Welxxv1f6bFFS+/bts+pHr1q1ytoeHBxMWloavr6+NkYnysrZs2eZPXs2pmkyf/58MjMzrX09evTAMAwMw6BZs2a2xSiJN9KJCyE8lwcn3mOAXGAoUF0Sb+EuBw8etKpmNG7cmO+++w5wzAO/9957iYyMZOTIkdSuXdveQEWppKenM3fuXEzTJDExscDqmN26dcMwDKKjo7n++uvdGpck3kgnLoTwXJ6aeOdRSr0BNJHEW9ghOzsbHx8fAObOnUtkZCTw+/L2hmEQFRVFcHCwnWGKUjp//jzz5s3DNE3mzJnD+fPnrX2dO3e2RsJbtmxZ7rFI4o104kIIz1VVEm+l1ARgAkBYWFiXAwcOuCE6UZUcP36cuLg4YmNjWbp0qVW6ztvbmwEDBhAXF1fpVlGsijIyMliwYAGmaTJ79mzS09Otfe3bt7eS8DZt2pTL+3vUypVKKX+l1OdKqQNKqXSl1Aal1PB8+wcqpbYrpTKUUkuUUvZN4hFCCFFmtNafaq27aq27hoSE2B2OqIRCQkJ4+OGH+fHHHzly5AiffvopQ4YMASAlJaVA0v3dd99x5MgRu0IVpRAQEMDo0aOZPn06x44dIyEhgfvuu4+goCA2b97Mq6++Stu2bWnTpg2vvvoqmzdvpiINMrt1xFspFQg8C3wBHAQigP/heDjnHLAHeBCYDUwB+mite1ztuDLiLUTll9QgiayjWZdt9w31pdeRXjZEVDYq4oi3UmopcGsRu5O01r3ztZWpJqJCO3nyJAcOHOCmm24CYP/+/Vx33XUopejTp49VNaNx48Y2RypK49KlS/z444+YpsnMmTM5deqUta9ly5bWSHinTp1KtTCTx081UUptAl4HgoFxWuuezu2BwAmgs9Z6+xUOIZ24EFXAUrW0yH39dD+3xVHWKmLiXRKSeAtPk5yczEsvvcT8+fOt0nUAvXr1Ijo6mgcffFCmpHi4rKwslixZgmmazJgxgxMnTlj7mjdvbiXhXbt2LXES7lFTTQpTSoUCLYFkoC3wa94+rfV5HCPgbe2JTgghRFGUUj5KqWqAN+CtlKqmlPKxOy4hrqZt27bMmjWL48ePM336dEaPHk21atVISkripZdeKpCInT592sZIxbXy9fVlyJAhfPrpp6SlpfHjjz/ypz/9idDQUPbu3cv/+3//j+7du3PdddfxzDPPsHLlSuuZgPJm24i3UsoXmAfs0Vo/pJT6HDiutX4hX5sk4D9a6y9cfL88qCNEFSIj3hWLUuo14NVCm1/XWr92te+VEW9R0Zw7d47ExEQOHjzIxIkTAcfUhQYNGhQYIb3hhhtsjlSURk5ODklJSZimSVxcHKmpqda+Jk2aEB0djWEY9OzZEy8v12PTHjnVRCnlBXwD1AKitNZZSql/Ab5a60fytdsMvKa1jrvS8aQTF6Lyk8S78pA+W3iCTZs20atXL86dO2dt69ixIzExMRiGwY033mhjdKK0cnNzWblyJbGxsZimyaFDh6x9DRs2ZMyYMRiGQZ8+ffD29rb2eVzirRz3cP4LhAMRWusLzu0TgPu11r2crwOB48BNMsdbCCGJd+UhfbbwFBcuXGDhwoWYpklCQgK//fabtW/r1q20bt3axuhEWcnNzWXNmjWYpolpmuzfv9/aV79+fUaPHo1hGPTr1w9fX1+PS7w/BjoBg7TW5/JtDwF2A+OBuTgeuLxVqpoIIUCqmlQm0mcLT5SZmcmiRYswTZOtW7eycuVKaz74vffeS3h4OIZh0KFDh1JVzRD20lqzfv16TNMkNjaWPXv2WPuCg4M5efKk5yTezrrc+4FMIDvfroe01tOVUoOA94FmwCocVU72X+240okLITyVJN5CeB6ttZVcp6SkEBYWZu1r0aKFNSe8c+fOkoR7MK01mzZtspLwHTt2AHhO4l1epBMXQngqSbyF8GzZ2dksXboU0zSJj4/n+PHj1r7rrruO+Ph4OnXqZF+AokxorUlOTqZ9+/aeW05QCCGEEMKT+fj4MGjQID7++GPS0tJYsmQJjz76KA0aNODw4cM0b97cavvdd9+RlJTkttJ1ouwopWjXrl2pjyOJtxBCCCFEGfD29qZfv368//77HD58mPXr11OrVi3AsajLo48+Su/evWnatClPPPEEP/30Ezk5OTZHLdxJEm8hhBBCiDLm5eVF27a/rwGYkZHB/fffT1hYGKmpqfz73//m1ltvpUmTJjz66KPs2rXLxmiFu0jiLYQQQghRzoKCgvj73//O/v37Wb16Nc899xzNmzfnyJEjfPjhh6Snp1ttU1JSyMq6vIqT8HyyvK8QQgghhJsopejWrRvdunXj7bffZuPGjcyfP5/OnTtbbe666y62bdvGqFGjMAyDgQMH4ufnV7I3ys6GlBRITYVLl8DPDxo1gqZNwUfSP7tIVRMhhLCRVDURQuR38eJFunTpwtatW61tQUFBREVFYRgGQ4YMwd/fv+gDaA1btkDe1JX8c8jzVmBs0QLatQMpdVhipe2zZaqJEEIIIUQFUa1aNZKTk0lOTub111+nffv2nD17lq+++oqRI0fy7bffFv3NWkNSkiPpzskpmHTD79t27XK0qwSDr55GEm8hhBBCiAqmTZs2/OUvf2HTpk1s376dN998k27dujFy5EirzcSJE7njjjswTZPz5887RrqPHbs84S4sJ8fRbsuWcv4UojCZaiKEEDaSqSZCiGuRm5tLw4YNOXbsGADVq1cnomNHjJtvZsRNN7HpiSZknb18fNU3KJde/znpeOHtDSNHypzvEpCpJkIIIYQQVYyXlxerVq3ib3/7Gz169ODChQvErVzJXf/6F/UffJCEswtcft9lyXhKihuiFXkk8RZCCCGE8EDh4eE888wz/PLLLxyMi+P/xo2jd6tWZGZn04xmVrtf+IX5zCed9IIHyMlxVD0RbiP3FoQQQgghPFzTOnV4MiKCJyMiSD11ih0Pt7T2fcd3/MqveONNF7owYfFNRHXtSr1atUDqhbuVJN5CCCGEEJ4uX53vRnXrspPfSwUOZCDeeLORjaxmNas/Xs1DXl70b9uWP993H8P79bMh4KpJEm8hhBBCCE/XqBEcPeqyosltzv/OcIYkktjc8Qd+3LKFRZs3c2d2ttUuJSUFHx8fGjZs6M7IqxSZ4y2EEEII4emaNi3w0jco97ImtanNqKDhzH/5ZY7+5z9MffRRRv3hD9b+d955h8aNG9O3b1/ee+89Dh06VO5hVzUy4i2EEEII4el8fBwrUjoXz7FKBhahblAQ4x56CEJDrW2XLl3Cz8+P5cuXs3z5cp588kluueUWDMMgOjqaZs2aXeGIojhkxFsIIYQQojJo1w7q1/99afiieHs72rVrV2Dzp59+yrFjx/jmm28YM2YM1apV45dffuGZZ57hX//6l9WuMqwBYxdJvIUQQgghKgOloFcvx8i3t/flCbiPj2NbixaOdkpddohatWpx1113ERcXx/Hjx/n++++5/fbbueOOO6w2n3zyCV26dOGtt95i586d5f2pKhVZuVIIIWwkK1cKIcpFdrZjcZzUVEfJQF9fxwOYTZuWeqXKyMhI5s6da73u0KEDhmFgGAatW7cubeQVWmn7bEm8hRDCRpJ4CyE8zcWLF1m4cCGmaTJr1ix+++03a98DDzzAZ599ZmN05UuWjBdCCCGEEG5TrVo1Ro4cyVdffcWxY8eYO3cu48aNo3bt2nTv3t1qt27dOiZNmsTGjRtlXriTJN5CCCGEEOKa+Pv7ExERwdSpUzl69Cj33XeftW/atGm8+eabdO7cmRYtWvDCCy+wdu3aKp2ES+IthBBCCCFKzc/Pj2rVqlmvY2JiePjhh6lfvz579uzhnXfeoVu3blx33XW8/fbbNkZqH0m8hRBCCCFEmevVqxcfffQRqampLFmyhEcffZQGDRpw4MABUlNTrXanT58mKSmJ3NzLF/2pbCTxFkIIIYQQ5cbb25t+/frx/vvvc/jwYZYvX85jjz1m7Y+Li6N37940adKExx9/nGXLlpGTk2NjxOVHEm8hhBBCCOEWXl5e9O7dm5YtW1rblFI0a9aMtLQ03n//ffr160ejRo3405/+xJIlS2yMtuxJ4i2EEEIIIWzzwAMPsG/fPtasWcPzzz9P8+bNOXbsGB9//DFTpkyx2uXm5pKVlWVjpKUnibcQQgghhLCVUoquXbvy9ttvs3v3bjZs2MBLL73EQw89ZLVZvXo1oaGhjB8/nsTERC5dumRjxNemdEsXCSGEEEIIUYaUUnTq1IlOnToV2L5kyRJOnz7N1KlTmTp1KkFBQYwcORLDMBgyZEiBiioVVYUb8VZK1VVKzVBKnVdKHVBK3W13TEIIIX6nlPJXSn3u7KPTlVIblFLD7Y5LCFG5vfjiiyQnJzN58mQ6dOjA2bNnmTZtGlFRUXTo0MEj6oNXuMQb+AC4BIQCY4GPlFJt7Q1JCCFEPj5ACnArEAS8AnyvlAq3MyghROXXpk0bXnnlFX799Vd27NhhLdAzaNAglFIAnDp1ijvvvJPY2FjOnz9vc8QFqYp0daCUCgROA+201jud26YBh7XWLxT1fV27dtVr1651U5RCCFF2lFLrtNZd7Y6jtJRSm4DXtdZxV2srfbYQoqxlZWXh6+sLwNSpUxk/fjwA1atXZ/jw4RiGQWRkJDVr1izV+5S2z65oI94tgZy8pNvpV0BGvIUQooJSSoXi6L+T7Y5FCFE15SXdAIMHD+bvf/87t9xyCxcuXCA+Pp67776bkJAQRo8eTXZ2tm1xVrSHK2sAZwttOwtcdnmilJoATHC+zFRKbSnn2NyhHnDC7iDKiHyWiqeyfA6oXJ/lRrsDKA2llC8wHfhSa739Cu08oc+uqD9XElfJSFwlU2XiyszMZObMmQWS9GtQqj67oiXe54BahbbVAtILN9Rafwp8CqCUWltJbtVWis8B8lkqosryOaDyfRa7YyhMKbUUx/xtV5K01r2d7byAaTiey3msiPaAZ/TZElfJSFwlI3GVTEWOqzTfX9ES752Aj1KqhdZ6l3NbR+T2pRBCuI3Wut/V2ijHU0yf43gQPkJr7dmrWgghhBtUqDneWuvzQDwwWSkVqJTqBUThGFERQghRcXwEtAZu01pfsDsYIYTwBBUq8XZ6BKgOHAP+B/xJa321Ee9Pyz0q96gsnwPks1REleVzgHwWWymlmgEPAZ2AI0qpc86vscU8REX9zBJXyUhcJSNxlUyljKtClRMUQgghhBCisqqII95CCCGEEEJUOpJ4CyGEEEII4QYem3grpeoqpWYopc4rpQ4ope62O6ZrpZR6TCm1VimVqZT6wu54rpVSyl8p9bnz7yNdKbVBKTXc7riulVLqa6VUmlLqN6XUTqXUg3bHVBpKqRZKqYtKqa/tjuVaKaWWOj9D3pziHXbHVBpKqTuVUtuc/dgepVQfu2Mqa9fSLyilnlZKHVFKnVVK/Vcp5V9OsRW771VKjVNK5eT72TunlOpnd1zO9u46X8X+vVue56uEcbjl3JQkLnf+LDnfryQ/5+48X8WKy4bzVaI+q6TnzGMTb+ADHLVjQ4GxwEdKKU9d4TIVeAP4r92BlJIPkIKj/m8Q8ArwvVIq3M6gSuEtIFxrXQsYCbyhlOpic0yl8QGwxu4gysBjWusazi+PXXxGKTUYeAf4A45FwvoCe20NqnyUqF9QSg0FXgAGAuFAc+D1coqtpH3vL/l+9mporZfaHZebz1dJf++W1/kqVhxuPjfFjsvJXT9LUMyfJxvOV0n+/bnzfBW7z7qWc+aRibdSKhCIBl7RWp/TWq8AEoB77Y3s2mit47XWM4GTdsdSGlrr81rr17TW+7XWuVrrOcA+wCOTVa11stY6M++l8+t6G0O6ZkqpO4EzwI82hyJ+9zowWWu90vnv5bDW+rDdQZW1a+gX7gc+d/77Ow1MAcaVU2wVsu8tYVxuOV8V5fduCeNw289SRTk/rpTg58lt56uEcblVCfusEp8zj0y8gZZAjtZ6Z75tvwKeOuJdKSmlQnH8XXnsAkhKqQ+VUhnAdiANSLQ5pBJTStUCJgPP2B1LGXlLKXVCKZVUnrcby5NSyhvoCoQopXYrpQ4ppd5XSlW3O7byVox+oS2O/jzPr0CoUiq4vGMrhs7On72dSqlXlFIVYRE6d52va/m9Wx7nqyRxuPNnqaTnpyr/LF0L287XVfqsEp8zT028awBnC207i+N2ragAlFK+wHTgS631drvjuVZa60dw/Fz1wbG4U+aVv6NCmoLjijzF7kDKwPM4buU1xlFLdbZSyhPvQoQCvoCB42erE9AZmGRjTOWumP1C4f497//t7t9/AtoB9XGMbN4FPGtrRA7uOl8l/b1bXuerJHG482epJHFV9Z+lkrLtfBWjzyrxOfPUxPscUKvQtlpAug2xiEKUUl44Vhu9BDxmczilprXOcd42bAL8ye54SkIp1QkYBPzT5lDKhNZ6ldY6XWudqbX+EkgCIuyO6xrkrfT4b611mtb6BPAPPPCzKMcDr7qIrxX52hW3Xyjcv+f9f4n69+LGVVxa671a633OW8+bcdxFMkp6nLKOC/edrxL93i2r8+VCSeIok3NT1nGV47kpLXeer2Kz63wVs88q8Tnz1MR7J+CjlGqRb1tHPHhKQ2WhlFLA5zhG9KK11lk2h1SWfPC8Od79cDzwcVApdQSYCEQrpdbbGVQZ0oCyO4iScs4FPIQjfo+mte6ntVZFfPWGEvcLyTj68zwdgaNa6xLNAy1OXKV0TT975RCXu85XaX/vltW/1ZLEUSbnphziKqyi9GPuPF+lUe7nqwR9VonPmUcm3lrr8zhu+09WSgUqpXoBUTiuTDyOUspHKVUN8Aa8lVLVKsh8r2vxEdAauE1rfeFqjSsqpVR95Sj1VkMp5e18cvkuYLHdsZXQpzguFjo5vz4G5gJD7Qvp2iilaiulhub9+1CO5cn7Agvsju0aTQUed/6s1QGeAubYG1K5KUm/8BXwgFKqjfO8TAK+KI+gStL3KqWGO+d6opRqhaPSwSy748JN56ukv3fL63yVMA63/SyVJC53/iw536O4P09uO18licvd58upuH1Wyc+Z1tojv4C6wEzgPHAQuNvumErxWV7j96oZeV+v2R3XNXyOZs7YL+K4/ZL3Ndbu2K7hs4QAy3BUAvkN2Az80e64yuBzvQZ8bXccpfg7WYPjFt4ZYCUw2O64SvF5fIEPnZ/lCPAeUM3uuMrhc16xXwDCnK/D8n3Pn4Gjzn97UwH/coqtyL63cFzA35wxncdR9nEy4Gt3XG4+X0X+3nXn+SoqDjvPTUnicufP0pV+nirA+SpWXDacryL7rLI4Z8r5TUIIIYQQQohy5JFTTYQQQgghhPA0kngLIYQQQgjhBpJ4CyGEEEII4QaSeAshhBBCCOEGkngLIYQQQgjhBpJ4CyGEEEII4QaSeAshhBBCCOEGkngLIYQQQgjhBpJ4iypPKbVQKaWVUmMKbVdKqS+c+962Kz4hhBC/kz5beDJZuVJUeUqpjsB6YAfQXmud49z+dxxLwf5Haz3BxhCFEEI4SZ8tPJmMeIsqT2v9KzANaA3cC6CUeglHB/498LB90QkhhMhP+mzhyWTEWwhAKdUE2AUcBf4G/BtYAIzUWl+yMzYhhBAFSZ8tPJWMeAsBaK0PAf8HNMPRgf8MjHHVgSulHlFK7VNKXVRKrVNK9XFvtEIIUbVJny08lSTeQvzueL7/f0BrnVG4gVLqDuBfwF+Bzjg6+3lKqTD3hCiEEMJJ+mzhcWSqiRCAUuouYDqO25YNgI+11n9y0W4VsElr/cd823YBptb6RXfFK4QQVZn02cJTyYi3qPKUUhHAl0Ay0AHYDjyolGpVqJ0f0AVYWOgQC4GebghVCCGqPOmzhSeTxFtUaUqp3oAJHAKGaK2PA68APkDhOrD1AG8cIyz55Y24CCGEKEfSZwtPJ4m3qLKctWDnAGeBwVrrNACttQmsBaKKeAin8Pws5WKbEEKIMiR9tqgMJPEWVZJS6gYcpac0MFRrvadQk7y5f+/m23YCyOHykZL6XD6iIoQQooxIny0qC3m4UogScD6o82v+VdGUUjuBOHlQRwghKhbps0VF42N3AEJ4mH8A05RSq4EkHCukNQI+tjUqIYQQrkifLSoUSbyFKAGt9XdKqWBgEtAQ2AJEaK0P2BuZEEKIwqTPFhWNTDURQgghhBDCDeThSiGEEEIIIdxAEm8hhBBCCCHcQBJvIYQQQggh3EASbyGEEEIIIdxAEm8hhBBCCCHcQBJvIYQQQggh3EASbyGEEEIIIdxAEm8hhBBCCCHcQBJvIYQQQggh3OD/A8+iAyP+gykHAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "Xs = np.array([[1, 50], [5, 20], [3, 80], [5, 60]]).astype(np.float64)\n",
    "ys = np.array([0, 0, 1, 1])\n",
    "svm_clf = SVC(kernel=\"linear\", C=100)\n",
    "svm_clf.fit(Xs, ys)\n",
    "\n",
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(Xs[:, 0][ys==1], Xs[:, 1][ys==1], \"bo\")\n",
    "plt.plot(Xs[:, 0][ys==0], Xs[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, 0, 6)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.ylabel(\"$x_1$  \", fontsize=20, rotation=0)\n",
    "plt.title(\"Unscaled\", fontsize=16)\n",
    "plt.axis([0, 6, 0, 90])\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "X_scaled = scaler.fit_transform(Xs)\n",
    "svm_clf.fit(X_scaled, ys)\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X_scaled[:, 0][ys==1], X_scaled[:, 1][ys==1], \"bo\")\n",
    "plt.plot(X_scaled[:, 0][ys==0], X_scaled[:, 1][ys==0], \"ms\")\n",
    "plot_svc_decision_boundary(svm_clf, -2, 2)\n",
    "plt.xlabel(\"$x_0$\", fontsize=20)\n",
    "plt.title(\"Scaled\", fontsize=16)\n",
    "plt.axis([-2, 2, -2, 2])\n",
    "\n",
    "# save_fig(\"sensitivity_to_feature_scales_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 硬间隔-异常值敏感"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x194.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_outliers = np.array([[3.4, 1.3], [3.2, 0.8]])\n",
    "y_outliers = np.array([0, 0])\n",
    "Xo1 = np.concatenate([X, X_outliers[:1]], axis=0)\n",
    "yo1 = np.concatenate([y, y_outliers[:1]], axis=0)\n",
    "Xo2 = np.concatenate([X, X_outliers[1:]], axis=0)\n",
    "yo2 = np.concatenate([y, y_outliers[1:]], axis=0)\n",
    "\n",
    "svm_clf2 = SVC(kernel=\"linear\", C=10**9)\n",
    "svm_clf2.fit(Xo2, yo2)\n",
    "\n",
    "plt.figure(figsize=(12,2.7))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.plot(Xo1[:, 0][yo1==1], Xo1[:, 1][yo1==1], \"bs\")\n",
    "plt.plot(Xo1[:, 0][yo1==0], Xo1[:, 1][yo1==0], \"yo\")\n",
    "plt.text(0.3, 1.0, \"Impossible!\", fontsize=24, color=\"red\")\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[0][0], X_outliers[0][1]),\n",
    "             xytext=(2.5, 1.7),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(Xo2[:, 0][yo2==1], Xo2[:, 1][yo2==1], \"bs\")\n",
    "plt.plot(Xo2[:, 0][yo2==0], Xo2[:, 1][yo2==0], \"yo\")\n",
    "plot_svc_decision_boundary(svm_clf2, 0, 5.5)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.annotate(\"Outlier\",\n",
    "             xy=(X_outliers[1][0], X_outliers[1][1]),\n",
    "             xytext=(3.2, 0.08),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=16,\n",
    "            )\n",
    "plt.axis([0, 5.5, 0, 2])\n",
    "\n",
    "# save_fig(\"sensitivity_to_outliers_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 软间隔分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下的 Scikit-Learn 代码加载了内置的鸢尾花（Iris）数据集，缩放特征，并训练一个线性 SVM 模型（使用LinearSVC类，超参数C=1，hinge 损失函数）来检测 Virginica 鸢尾花"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc', LinearSVC(C=1, loss='hinge'))])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.svm import LinearSVC\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "X = iris[\"data\"][:,(2,3)] # petal length, petal width\n",
    "y = (iris[\"target\"]==2).astype(np.float64) # Iris-Virginica\n",
    "\n",
    "svm_clf = Pipeline((\n",
    "    (\"scaler\", StandardScaler()),\n",
    "    (\"linear_svc\", LinearSVC(C=1, loss = 'hinge')),\n",
    "))\n",
    "\n",
    "svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_clf.predict([[5.5,1.7]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "将不同正则化设定进行比较"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\svm\\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('linear_svc',\n",
       "                 LinearSVC(C=100, loss='hinge', random_state=42))])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler = StandardScaler()\n",
    "svm_clf1 = LinearSVC(C=1, loss=\"hinge\", random_state=42)\n",
    "svm_clf2 = LinearSVC(C=100, loss=\"hinge\", random_state=42)\n",
    "\n",
    "scaled_svm_clf1 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf1),\n",
    "    ])\n",
    "scaled_svm_clf2 = Pipeline([\n",
    "        (\"scaler\", scaler),\n",
    "        (\"linear_svc\", svm_clf2),\n",
    "    ])\n",
    "\n",
    "scaled_svm_clf1.fit(X, y)\n",
    "scaled_svm_clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to unscaled parameters\n",
    "b1 = svm_clf1.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "b2 = svm_clf2.decision_function([-scaler.mean_ / scaler.scale_])\n",
    "w1 = svm_clf1.coef_[0] / scaler.scale_\n",
    "w2 = svm_clf2.coef_[0] / scaler.scale_\n",
    "svm_clf1.intercept_ = np.array([b1])\n",
    "svm_clf2.intercept_ = np.array([b2])\n",
    "svm_clf1.coef_ = np.array([w1])\n",
    "svm_clf2.coef_ = np.array([w2])\n",
    "\n",
    "# Find support vectors (LinearSVC does not do this automatically)\n",
    "t = y * 2 - 1\n",
    "support_vectors_idx1 = (t * (X.dot(w1) + b1) < 1).ravel()\n",
    "support_vectors_idx2 = (t * (X.dot(w2) + b2) < 1).ravel()\n",
    "svm_clf1.support_vectors_ = X[support_vectors_idx1]\n",
    "svm_clf2.support_vectors_ = X[support_vectors_idx2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4.0, 6.0, 0.8, 2.8)"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x230.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,3.2))\n",
    "plt.subplot(121)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "plot_svc_decision_boundary(svm_clf2, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf2.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\", label=\"Iris-Virginica\")\n",
    "plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\", label=\"Iris-Versicolor\")\n",
    "plot_svc_decision_boundary(svm_clf1, 4, 6)\n",
    "plt.xlabel(\"Petal length\", fontsize=14)\n",
    "plt.ylabel(\"Petal width\", fontsize=14)\n",
    "plt.legend(loc=\"upper left\", fontsize=14)\n",
    "plt.title(\"$C = {}$\".format(svm_clf1.C), fontsize=16)\n",
    "plt.axis([4, 6, 0.8, 2.8])\n",
    "# save_fig(\"regularization_plot\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "作为一种选择，你可以在 SVC 类，使用SVC(kernel=\"linear\", C=1)，但是它比较慢，尤其在较大的训练集上，所以一般不被推荐。另一个选择是使用SGDClassifier类，即SGDClassifier(loss=\"hinge\", alpha=1/(m*C))。它应用了随机梯度下降（SGD 见第四章）来训练一个线性 SVM 分类器。尽管它不会和LinearSVC一样快速收敛，但是对于处理那些不适合放在内存的大数据集是非常有用的，或者处理在线分类任务同样有用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 非线性支持向量机分类"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "尽管线性 SVM 分类器在许多案例上表现得出乎意料的好，但是很多数据集并不是线性可分的。一种处理非线性数据集方法是增加更多的特征，例如多项式特征（正如你在第 4 章所做的那样）；在某些情况下可以变成线性可分的数据。在图 5-5 的左图中，它只有一个特征x1的简单的数据集，正如你看到的，该数据集不是线性可分的。但是如果你增加了第二个特征 x2=(x1)^2，产生的 2D 数据集就能很好的线性可分。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X1D = np.linspace(-4, 4, 9).reshape(-1, 1)\n",
    "X2D = np.c_[X1D, X1D**2]\n",
    "y = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.plot(X1D[:, 0][y==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][y==1], np.zeros(5), \"g^\")\n",
    "plt.gca().get_yaxis().set_ticks([])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.2, 0.2])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(X2D[:, 0][y==0], X2D[:, 1][y==0], \"bs\")\n",
    "plt.plot(X2D[:, 0][y==1], X2D[:, 1][y==1], \"g^\")\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "plt.gca().get_yaxis().set_ticks([0, 4, 8, 12, 16])\n",
    "plt.plot([-4.5, 4.5], [6.5, 6.5], \"r--\", linewidth=3)\n",
    "plt.axis([-4.5, 4.5, -1, 17])\n",
    "\n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "# save_fig(\"higher_dimensions_plot\", tight_layout=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "为了实施这个想法，通过 Scikit-Learn，你可以创建一个流水线（Pipeline）去包含多项式特征（PolynomialFeatures）变换（在 121 页的“Polynomial Regression”中讨论），然后一个StandardScaler和LinearSVC。让我们在卫星数据集（moons datasets）测试一下效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "X, y = make_moons(n_samples=100, noise=0.15, random_state=42)\n",
    "\n",
    "def plot_dataset(X, y, axes):\n",
    "    plt.plot(X[:, 0][y==0], X[:, 1][y==0], \"bs\")\n",
    "    plt.plot(X[:, 0][y==1], X[:, 1][y==1], \"g^\")\n",
    "    plt.axis(axes)\n",
    "    plt.grid(True, which='both')\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "    plt.ylabel(r\"$x_2$\", fontsize=20, rotation=0)\n",
    "\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\dongc\\.conda\\envs\\MachineLearning\\lib\\site-packages\\sklearn\\svm\\_base.py:977: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  \"the number of iterations.\", ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),\n",
       "                ('scaler', StandardScaler()),\n",
       "                ('svm_clf', LinearSVC(C=10, loss='hinge'))])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.datasets import make_moons\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "\n",
    "polynomial_svm_clf = Pipeline((\n",
    "        (\"poly_features\", PolynomialFeatures(degree=3)),\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", LinearSVC(C=10, loss=\"hinge\"))\n",
    "    ))\n",
    "polynomial_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_predictions(clf, axes):\n",
    "    x0s = np.linspace(axes[0], axes[1], 100)\n",
    "    x1s = np.linspace(axes[2], axes[3], 100)\n",
    "    x0, x1 = np.meshgrid(x0s, x1s)\n",
    "    X = np.c_[x0.ravel(), x1.ravel()]\n",
    "    y_pred = clf.predict(X).reshape(x0.shape)\n",
    "    y_decision = clf.decision_function(X).reshape(x0.shape)\n",
    "    plt.contourf(x0, x1, y_pred, cmap=plt.cm.brg, alpha=0.2)\n",
    "    plt.contourf(x0, x1, y_decision, cmap=plt.cm.brg, alpha=0.1)\n",
    "\n",
    "plot_predictions(polynomial_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "\n",
    "# save_fig(\"moons_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 多项式核"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "当你使用 SVM 时，你可以运用一个被称为“核技巧”（kernel trick）的神奇数学技巧。它可以取得就像你添加了许多多项式，甚至有高次数的多项式，一样好的结果。所以不会大量特征导致的组合爆炸，因为你并没有增加任何特征。这个技巧可以用 SVC 类来实现。让我们在卫星数据集测试一下效果。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=1, kernel='poly'))])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "poly_kernel_svm_clf = Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=3, coef0=1, C=5))\n",
    "    ))\n",
    "poly_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=5, coef0=100, degree=10, kernel='poly'))])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "poly100_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"poly\", degree=10, coef0=100, C=5))\n",
    "    ])\n",
    "poly100_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plot_predictions(poly_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=3, r=1, C=5$\", fontsize=18)\n",
    "\n",
    "plt.subplot(122)\n",
    "plot_predictions(poly100_kernel_svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "plt.title(r\"$d=10, r=100, C=5$\", fontsize=18)\n",
    "\n",
    "# save_fig(\"moons_kernelized_polynomial_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这段代码用 3 阶的多项式核训练了一个 SVM 分类器，即图 5-7 的左图。右图是使用了 10 阶的多项式核 SVM 分类器。很明显，如果你的模型过拟合，你可以减小多项式核的阶数。相反的，如果是欠拟合，你可以尝试增大它。超参数coef0控制了高阶多项式与低阶多项式对模型的影响。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "通用的方法是用网格搜索（grid search 见第 2 章）去找到最优超参数。首先进行非常粗略的网格搜索一般会很快，然后在找到的最佳值进行更细的网格搜索。对每个超参数的作用有一个很好的理解可以帮助你在正确的超参数空间找到合适的值。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 增加相似特征"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "另一种解决非线性问题的方法是使用相似函数（similarity funtion）计算每个样本与特定地标（landmark）的相似度。例如，让我们来看看前面讨论过的一维数据集，并在x1=-2和x1=1之间增加两个地标（图 5-8 左图）。接下来，我们定义一个相似函数，即高斯径向基函数（Gaussian Radial Basis Function，RBF），设置γ = 0.3（见公式 5-1）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RBF\n",
    "\n",
    "$\\phi_{\\gamma}(x, \\ell) = exp(-\\gamma \\|x - \\ell \\|^2)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "它是个从 0 到 1 的钟型函数，值为 0 的离地标很远，值为 1 的在地标上。现在我们准备计算新特征。例如，我们看一下样本x1=-1：它距离第一个地标距离是 1，距离第二个地标是 2。因此它的新特征为x2=exp(-0.3 × (1^2))≈0.74和x3=exp(-0.3 × (2^2))≈0.30。图 5-8 右边的图显示了特征转换后的数据集（删除了原始特征），正如你看到的，它现在是线性可分了。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def gaussian_rbf(x, landmark, gamma):\n",
    "    return np.exp(-gamma * np.linalg.norm(x - landmark, axis=1)**2)\n",
    "\n",
    "gamma = 0.3\n",
    "\n",
    "x1s = np.linspace(-4.5, 4.5, 200).reshape(-1, 1)\n",
    "x2s = gaussian_rbf(x1s, -2, gamma)\n",
    "x3s = gaussian_rbf(x1s, 1, gamma)\n",
    "\n",
    "XK = np.c_[gaussian_rbf(X1D, -2, gamma), gaussian_rbf(X1D, 1, gamma)]\n",
    "yk = np.array([0, 0, 1, 1, 1, 1, 1, 0, 0])\n",
    "\n",
    "plt.figure(figsize=(11, 4))\n",
    "\n",
    "plt.subplot(121)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.scatter(x=[-2, 1], y=[0, 0], s=150, alpha=0.5, c=\"red\")\n",
    "plt.plot(X1D[:, 0][yk==0], np.zeros(4), \"bs\")\n",
    "plt.plot(X1D[:, 0][yk==1], np.zeros(5), \"g^\")\n",
    "plt.plot(x1s, x2s, \"g--\")\n",
    "plt.plot(x1s, x3s, \"b:\")\n",
    "plt.gca().get_yaxis().set_ticks([0, 0.25, 0.5, 0.75, 1])\n",
    "plt.xlabel(r\"$x_1$\", fontsize=20)\n",
    "plt.ylabel(r\"Similarity\", fontsize=14)\n",
    "plt.annotate(r'$\\mathbf{x}$',\n",
    "             xy=(X1D[3, 0], 0),\n",
    "             xytext=(-0.5, 0.20),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.text(-2, 0.9, \"$x_2$\", ha=\"center\", fontsize=20)\n",
    "plt.text(1, 0.9, \"$x_3$\", ha=\"center\", fontsize=20)\n",
    "plt.axis([-4.5, 4.5, -0.1, 1.1])\n",
    "\n",
    "plt.subplot(122)\n",
    "plt.grid(True, which='both')\n",
    "plt.axhline(y=0, color='k')\n",
    "plt.axvline(x=0, color='k')\n",
    "plt.plot(XK[:, 0][yk==0], XK[:, 1][yk==0], \"bs\")\n",
    "plt.plot(XK[:, 0][yk==1], XK[:, 1][yk==1], \"g^\")\n",
    "plt.xlabel(r\"$x_2$\", fontsize=20)\n",
    "plt.ylabel(r\"$x_3$  \", fontsize=20, rotation=0)\n",
    "plt.annotate(r'$\\phi\\left(\\mathbf{x}\\right)$',\n",
    "             xy=(XK[3, 0], XK[3, 1]),\n",
    "             xytext=(0.65, 0.50),\n",
    "             ha=\"center\",\n",
    "             arrowprops=dict(facecolor='black', shrink=0.1),\n",
    "             fontsize=18,\n",
    "            )\n",
    "plt.plot([-0.1, 1.1], [0.57, -0.1], \"r--\", linewidth=3)\n",
    "plt.axis([-0.1, 1.1, -0.1, 1.1])\n",
    "    \n",
    "plt.subplots_adjust(right=1)\n",
    "\n",
    "# save_fig(\"kernel_method_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 高斯 RBF 核"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "就像多项式特征法一样，相似特征法对各种机器学习算法同样也有不错的表现。但是在所有额外特征上的计算成本可能很高，特别是在大规模的训练集上。然而，“核” 技巧再一次显现了它在 SVM 上的神奇之处：高斯核让你可以获得同样好的结果成为可能，就像你在相似特征法添加了许多相似特征一样，但事实上，你并不需要在 RBF 添加它们。我们使用 SVC 类的高斯 RBF 核来检验一下。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('scaler',\n",
       "                 StandardScaler(copy=True, with_mean=True, with_std=True)),\n",
       "                ('svm_clf',\n",
       "                 SVC(C=0.001, break_ties=False, cache_size=200,\n",
       "                     class_weight=None, coef0=0.0,\n",
       "                     decision_function_shape='ovr', degree=3, gamma=5,\n",
       "                     kernel='rbf', max_iter=-1, probability=False,\n",
       "                     random_state=None, shrinking=True, tol=0.001,\n",
       "                     verbose=False))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline((\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ))\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Phi(-1.0, -2) = [0.74081822]\n",
      "Phi(-1.0, 1) = [0.30119421]\n"
     ]
    }
   ],
   "source": [
    "x1_example = X1D[3, 0]\n",
    "for landmark in (-2, 1):\n",
    "    k = gaussian_rbf(np.array([[x1_example]]), np.array([[landmark]]), gamma)\n",
    "    print(\"Phi({}, {}) = {}\".format(x1_example, landmark, k))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x504 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "\n",
    "gamma1, gamma2 = 0.1, 5\n",
    "C1, C2 = 0.001, 1000\n",
    "hyperparams = (gamma1, C1), (gamma1, C2), (gamma2, C1), (gamma2, C2)\n",
    "\n",
    "svm_clfs = []\n",
    "for gamma, C in hyperparams:\n",
    "    rbf_kernel_svm_clf = Pipeline([\n",
    "            (\"scaler\", StandardScaler()),\n",
    "            (\"svm_clf\", SVC(kernel=\"rbf\", gamma=gamma, C=C))\n",
    "        ])\n",
    "    rbf_kernel_svm_clf.fit(X, y)\n",
    "    svm_clfs.append(rbf_kernel_svm_clf)\n",
    "\n",
    "plt.figure(figsize=(11, 7))\n",
    "\n",
    "for i, svm_clf in enumerate(svm_clfs):\n",
    "    plt.subplot(221 + i)\n",
    "    plot_predictions(svm_clf, [-1.5, 2.5, -1, 1.5])\n",
    "    plot_dataset(X, y, [-1.5, 2.5, -1, 1.5])\n",
    "    gamma, C = hyperparams[i]\n",
    "    plt.title(r\"$\\gamma = {}, C = {}$\".format(gamma, C), fontsize=16)\n",
    "\n",
    "# save_fig(\"moons_rbf_svc_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这个模型在图 5-9 的左下角表示。其他的图显示了用不同的超参数gamma (γ)和C训练的模型。增大γ使钟型曲线更窄（图 5-8 左图），导致每个样本的影响范围变得更小：即判定边界最终变得更不规则，在单个样本周围环绕。相反的，较小的γ值使钟型曲线更宽，样本有更大的影响范围，判定边界最终则更加平滑。所以γ是可调整的超参数：如果你的模型过拟合，你应该减小γ值，若欠拟合，则增大γ（与超参数C相似）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVM 回归"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 50\n",
    "X = 2 * np.random.rand(m, 1)\n",
    "y = (4 + 3 * X + np.random.randn(m, 1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(epsilon=1.5, random_state=42)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "\n",
    "svm_reg = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "svm_reg1 = LinearSVR(epsilon=1.5, random_state=42)\n",
    "svm_reg2 = LinearSVR(epsilon=0.5, random_state=42)\n",
    "svm_reg1.fit(X, y)\n",
    "svm_reg2.fit(X, y)\n",
    "\n",
    "def find_support_vectors(svm_reg, X, y):\n",
    "    y_pred = svm_reg.predict(X)\n",
    "    off_margin = (np.abs(y - y_pred) >= svm_reg.epsilon)\n",
    "    return np.argwhere(off_margin)\n",
    "\n",
    "svm_reg1.support_ = find_support_vectors(svm_reg1, X, y)\n",
    "svm_reg2.support_ = find_support_vectors(svm_reg2, X, y)\n",
    "\n",
    "eps_x1 = 1\n",
    "eps_y_pred = svm_reg1.predict([[eps_x1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SVM 算法应用广泛：不仅仅支持线性和非线性的分类任务，还支持线性和非线性的回归任务。技巧在于逆转我们的目标：限制间隔违规的情况下，不是试图在两个类别之间找到尽可能大的“街道”（即间隔）。SVM 回归任务是限制间隔违规情况下，尽量放置更多的样本在“街道”上。“街道”的宽度由超参数ϵ控制。图 5-10 显示了在一些随机生成的线性数据上，两个线性 SVM 回归模型的训练情况。一个有较大的间隔（ϵ=1.5），另一个间隔较小（ϵ=0.5）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def plot_svm_regression(svm_reg, X, y, axes):\n",
    "    x1s = np.linspace(axes[0], axes[1], 100).reshape(100, 1)\n",
    "    y_pred = svm_reg.predict(x1s)\n",
    "    plt.plot(x1s, y_pred, \"k-\", linewidth=2, label=r\"$\\hat{y}$\")\n",
    "    plt.plot(x1s, y_pred + svm_reg.epsilon, \"k--\")\n",
    "    plt.plot(x1s, y_pred - svm_reg.epsilon, \"k--\")\n",
    "    plt.scatter(X[svm_reg.support_], y[svm_reg.support_], s=180, facecolors='#FFAAAA')\n",
    "    plt.plot(X, y, \"bo\")\n",
    "    plt.xlabel(r\"$x_1$\", fontsize=18)\n",
    "    plt.legend(loc=\"upper left\", fontsize=18)\n",
    "    plt.axis(axes)\n",
    "\n",
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_reg1, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "#plt.plot([eps_x1, eps_x1], [eps_y_pred, eps_y_pred - svm_reg1.epsilon], \"k-\", linewidth=2)\n",
    "plt.annotate(\n",
    "        '', xy=(eps_x1, eps_y_pred), xycoords='data',\n",
    "        xytext=(eps_x1, eps_y_pred - svm_reg1.epsilon),\n",
    "        textcoords='data', arrowprops={'arrowstyle': '<->', 'linewidth': 1.5}\n",
    "    )\n",
    "plt.text(0.91, 5.6, r\"$\\epsilon$\", fontsize=20)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_reg2, X, y, [0, 2, 3, 11])\n",
    "plt.title(r\"$\\epsilon = {}$\".format(svm_reg2.epsilon), fontsize=18)\n",
    "# save_fig(\"svm_regression_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(steps=[('scaler', StandardScaler()),\n",
       "                ('svm_clf', SVC(C=0.001, gamma=5))])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rbf_kernel_svm_clf = Pipeline([\n",
    "        (\"scaler\", StandardScaler()),\n",
    "        (\"svm_clf\", SVC(kernel=\"rbf\", gamma=5, C=0.001))\n",
    "    ])\n",
    "rbf_kernel_svm_clf.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearSVR(epsilon=1.5)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVR\n",
    "svm_reg = LinearSVR(epsilon=1.5)\n",
    "svm_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "处理非线性回归任务，你可以使用核化的 SVM 模型。比如，图 5-11 显示了在随机二次方的训练集，使用二次方多项式核函数的 SVM 回归。左图是较小的正则化（即更大的C值），右图则是更大的正则化（即小的C值）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "m = 100\n",
    "X = 2 * np.random.rand(m, 1) - 1\n",
    "y = (0.2 + 0.1 * X + 0.5 * X**2 + np.random.randn(m, 1)/10).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=100, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=0.01, degree=2, kernel='poly')"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVR\n",
    "\n",
    "svm_poly_reg1 = SVR(kernel=\"poly\", degree=2, C=100, epsilon=0.1)\n",
    "svm_poly_reg2 = SVR(kernel=\"poly\", degree=2, C=0.01, epsilon=0.1)\n",
    "svm_poly_reg1.fit(X, y)\n",
    "svm_poly_reg2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(9, 4))\n",
    "plt.subplot(121)\n",
    "plot_svm_regression(svm_poly_reg1, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg1.degree, svm_poly_reg1.C, svm_poly_reg1.epsilon), fontsize=18)\n",
    "plt.ylabel(r\"$y$\", fontsize=18, rotation=0)\n",
    "plt.subplot(122)\n",
    "plot_svm_regression(svm_poly_reg2, X, y, [-1, 1, 0, 1])\n",
    "plt.title(r\"$degree={}, C={}, \\epsilon = {}$\".format(svm_poly_reg2.degree, svm_poly_reg2.C, svm_poly_reg2.epsilon), fontsize=18)\n",
    "# save_fig(\"svm_with_polynomial_kernel_plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的代码的模型在图 5-11，其使用了 Scikit-Learn 的SVR类（支持核技巧）。在回归任务上，SVR类和SVC类是一样的，并且LinearSVR是和LinearSVC等价。LinearSVR类和训练集的大小成线性（就像LinearSVC类），当训练集变大，SVR会变的很慢（就像SVC类）"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
